{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e806df2e",
   "metadata": {},
   "source": [
    "# 5. 训练过程 Training process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 目录\n",
    "- [ 6.1 - 前言：需要几行代码就可以在GPU训练lamini](#6.1)\n",
    "- [ 6.2 - EleutherAI/pythia模型介绍](#6.2)\n",
    "- [ 6.3 - 微调实验准备](#6.3)\n",
    "  - [6.3.1 - 实验环境准备](#6.3.1)\n",
    "  - [6.3.2 - 导入 lamini 训练数据](#6.3.2)\n",
    "  - [6.3.3 - 设置模型、配置参数、分词器](#6.3.3)\n",
    "  - [6.3.4 - 加载基础模型](#6.3.4)\n",
    "  - [6.3.5 - 定义推理函数](#6.3.5)\n",
    "  - [6.3.6 - 使用基础模型](#6.3.6)\n",
    "  - [6.3.7 - 训练参数配置](#6.3.7)\n",
    "- [ 6.4 - 微调训练](#6.4)\n",
    "  - [6.4.1 - 开始训练](#6.4.1)\n",
    "  - [6.4.2 - 本地存储模型](#6.4.2)\n",
    "  - [6.4.3 - 运行微调过的模型](#6.4.3)\n",
    "  - [6.4.4 - 训练运行在线模型](#6.4.4)\n",
    "  - [6.4.5 - 用更大的模型探索效果](#6.4.5)\n",
    "  - [6.4.6 - 用更小的模型探索效果](#6.4.6)\n",
    "  - [6.4.7 - 使用三行代码微调lamini](#6.4.7)\n",
    "- [ 6.5 - 附表](#6.5)\n",
    "  - [6.5.1 - 笔者训练的效果](#6.5.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e1dbbb",
   "metadata": {
    "id": "ozJEJi6d06SG",
    "tags": []
   },
   "source": [
    "<a name='6.1'></a>\n",
    "## 6.1 前言：需要几行代码就可以在GPU训练lamini\n",
    "llama库文档地址：https://lamini-ai.github.io/ \n",
    "\n",
    "从使用角度讲只需要下面几行代码就可以完成模型的微调，但是要理解这个过程请完成后续的试验。\n",
    "\n",
    "```\n",
    "from llama import BasicModelRunner\n",
    "# 从基础模块导入模型\n",
    "model = BasicModelRunner(\"EleutherAI/pythia-410m\")\n",
    "# 导入训练数据\n",
    "model.load_data_from_jsonlines(\"lamini_docs.jsonl\")\n",
    "model.train()\n",
    "```\n",
    "\n",
    "<a name='6.2'></a>\n",
    "## 6.2 EleutherAI/pythia模型介绍\n",
    "Pythia缩放套件是一个模型集合，用于促进可解释性研究(见论文) https://arxiv.org/pdf/2304.01373.pdf 。包含70M、160M、410M、1B、1.4B、2.8B、6.9B、12B两套共8个型号。对于每个大小，有两个模型:一个在Pile上训练，另一个在数据集进行全局重复数据删除后在Pile上训练。所有8种模型大小都是在完全相同的数据上以完全相同的顺序进行训练的。我们还为每个模型提供154个中间检查点，作为分支托管在hug Face上。Pythia模型套件旨在促进大型语言模型的科学研究，特别是可解释性研究。尽管没有将下游性能作为设计目标，但我们发现这些模型的性能达到或超过了类似和相同尺寸的模型，例如OPT和GPT-Neo套件中的模型。关于以前的早期版本和命名约定的详细信息。之前，我们向公众发布了一个早期版本的Pythia套件。然而，我们决定重新训练模型套件来解决一些超参数差异。这个模型卡片列出了变化;进一步的讨论见Pythia论文的附录B。我们发现两个Pythia版本在基准测试性能上没有差异。旧的模型仍然可用，但我们建议重新训练套件，如果你刚刚开始使用Pythia。这是当前版本。请注意，Pythia套件中的所有模型都在2023年1月重新命名。为了清楚起见，在此模型卡中提供了一个比较新旧名称的表，以及确切的参数计数。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='6.2'></a>\n",
    "## 6.3 微调实验准备"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='6.3.1'></a>\n",
    "### 6.3.1 实验环境准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34ecdab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 安装环境  torch版本大家先试试本来的版本可不可行再安装\n",
    "!pip install -r requirements.txt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5ee3dd13",
   "metadata": {
    "height": 387
   },
   "outputs": [],
   "source": [
    "import datasets\n",
    "import tempfile\n",
    "import logging\n",
    "import random\n",
    "import config  # 这里的config是官方自带的  我们导入了\n",
    "import os\n",
    "import yaml\n",
    "import logging\n",
    "import time\n",
    "import torch\n",
    "import transformers\n",
    "\n",
    "from utilities import * # 这里的utilities是官方自带的  我们导入了\n",
    "from transformers import AutoTokenizer\n",
    "from transformers import AutoModelForCausalLM\n",
    "from transformers import TrainingArguments\n",
    "from transformers import AutoModelForCausalLM\n",
    "from llama import BasicModelRunner\n",
    "from llama import BasicModelRunner\n",
    "\n",
    "logger = logging.getLogger(__name__)\n",
    "global_config = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70991cda",
   "metadata": {},
   "source": [
    "<a name='6.3.2'></a>\n",
    "### 6.3.2 导入 lamini 训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2d1439e",
   "metadata": {
    "height": 64
   },
   "outputs": [],
   "source": [
    "dataset_name = \"lamini_docs.jsonl\"  # 这是导入微调的文件\n",
    "dataset_path = f\"/content/{dataset_name}\"\n",
    "use_hf = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1276f5",
   "metadata": {},
   "source": [
    "### lamini_docs.jsonl截取内容\n",
    "可以看到是关于llama的一些问题。可以看到是对llama为主题的做的问答对，也即是llama微调的内容。\n",
    "<br>那么对你来说这个内容可以是客服问题/或是和女仆的对话等等……\n",
    "<br>\n",
    "<br>送入数据结构：{\n",
    "    \"question\":\"问题\",\n",
    "    \"answer\":\"回答\"\n",
    "}\n",
    "<br>\n",
    "<br>问答例子：<br>\n",
    "{\n",
    "    \"question\": \"What are the different types of documents available in the repository (e.g., installation guide, API documentation, developer's guide)?\", <br>\n",
    "    \"answer\": \"Lamini has documentation on Getting Started, Authentication, Question Answer Model, Python Library, Batching, Error Handling, Advanced topics, and class documentation on LLM Engine available at https://lamini-ai.github.io/.\"}\n",
    "\n",
    "{\"question\": \"What is the recommended way to set up and configure the code repository?\", <br>\"answer\": \"Lamini can be downloaded as a python package and used in any codebase that uses python. Additionally, we provide a language agnostic REST API. We\\u2019ve seen users develop and train models in a notebook environment, and then switch over to a REST API to integrate with their production environment.\"}\n",
    "\n",
    "{\"question\": \"How can I find the specific documentation I need for a particular feature or function?\", <br>\"answer\": \"You can ask this model about documentation, which is trained on our publicly available docs and source code, or you can go to https://lamini-ai.github.io/.\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "09842900",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "dataset_path = \"lamini/lamini_docs\"\n",
    "use_hf = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c700b9ab",
   "metadata": {},
   "source": [
    "<a name='6.3.3'></a>\n",
    "### 6.3.3 设置模型、配置参数、分词器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e4c1e05f",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "# 先选了比较小的70m\n",
    "model_name = \"EleutherAI/pythia-70m\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "de1dbf01",
   "metadata": {
    "height": 200
   },
   "outputs": [],
   "source": [
    "training_config = {\n",
    "    \"model\": {\n",
    "        \"pretrained_name\": model_name,\n",
    "        \"max_length\" : 2048  # 最长输入\n",
    "    },\n",
    "    \"datasets\": {  # 数据设置\n",
    "        \"use_hf\": use_hf,\n",
    "        \"path\": dataset_path\n",
    "    },\n",
    "    \"verbose\": True\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0ea25015",
   "metadata": {
    "height": 132
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-31 23:37:45,860 - DEBUG - utilities - Config: datasets.path: lamini/lamini_docs\n",
      "datasets.use_hf: true\n",
      "model.max_length: 2048\n",
      "model.pretrained_name: EleutherAI/pythia-70m\n",
      "verbose: true\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tokenize True lamini/lamini_docs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-31 23:37:55,662 - DEBUG - fsspec.local - open file: C:/Users/pipi/.cache/huggingface/datasets/lamini___lamini_docs/default-a15134f5c9ebe39e/0.0.0/e58c486e4bad3c9cf8d969f920449d1103bbdf069a7150db2cf96c695aeca990/dataset_info.json\n",
      "2023-08-31 23:37:55,665 - DEBUG - fsspec.local - open file: C:/Users/pipi/.cache/huggingface/datasets/lamini___lamini_docs/default-a15134f5c9ebe39e/0.0.0/e58c486e4bad3c9cf8d969f920449d1103bbdf069a7150db2cf96c695aeca990/dataset_info.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset({\n",
      "    features: ['question', 'answer', 'input_ids', 'attention_mask', 'labels'],\n",
      "    num_rows: 1260\n",
      "})\n",
      "Dataset({\n",
      "    features: ['question', 'answer', 'input_ids', 'attention_mask', 'labels'],\n",
      "    num_rows: 140\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "# tokenizer.eos token是分词器对象的属性，它表示分词器所使用的特殊标记(token)中的\"end of sequence”(序列结束)标记。\n",
    "# 这个特殊标记在处理自然语言文本时经常用于表示句子或文本的结尾。\n",
    "# 在这段代码中，它被赋值给了tokenizer.pad token属性，将其用作填充标记(padding token)。\n",
    "train_dataset, test_dataset = tokenize_and_split_data(training_config, tokenizer)\n",
    "\n",
    "print(train_dataset)\n",
    "print(test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e939697e",
   "metadata": {},
   "source": [
    "<a name='6.3.4'></a>\n",
    "### 6.3.4 加载基础模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6c89e9c0",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "base_model = AutoModelForCausalLM.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "78ab1d4f",
   "metadata": {
    "height": 132
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-31 23:37:56,581 - DEBUG - __main__ - Select GPU device\n"
     ]
    }
   ],
   "source": [
    "# 验证cuda可用性\n",
    "device_count = torch.cuda.device_count()\n",
    "\n",
    "if device_count > 0:\n",
    "    logger.debug(\"Select GPU device\")\n",
    "    device = torch.device(\"cuda\")\n",
    "else:\n",
    "    logger.debug(\"Select CPU device\")\n",
    "    device = torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "233fcf1e",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GPTNeoXForCausalLM(\n",
       "  (gpt_neox): GPTNeoXModel(\n",
       "    (embed_in): Embedding(50304, 512)\n",
       "    (emb_dropout): Dropout(p=0.0, inplace=False)\n",
       "    (layers): ModuleList(\n",
       "      (0-5): 6 x GPTNeoXLayer(\n",
       "        (input_layernorm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (post_attention_layernorm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (post_attention_dropout): Dropout(p=0.0, inplace=False)\n",
       "        (post_mlp_dropout): Dropout(p=0.0, inplace=False)\n",
       "        (attention): GPTNeoXAttention(\n",
       "          (rotary_emb): GPTNeoXRotaryEmbedding()\n",
       "          (query_key_value): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (dense): Linear(in_features=512, out_features=512, bias=True)\n",
       "          (attention_dropout): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (mlp): GPTNeoXMLP(\n",
       "          (dense_h_to_4h): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (dense_4h_to_h): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (act): GELUActivation()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (embed_out): Linear(in_features=512, out_features=50304, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_model.to(device)\n",
    "# 模型送入GPU/CPU  可以看一下模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1144075",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name='6.3.5'></a>\n",
    "### 6.3.5 定义推理函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e80deb9c",
   "metadata": {
    "height": 421
   },
   "outputs": [],
   "source": [
    "def inference(text, model, tokenizer, max_input_tokens=1000, max_output_tokens=100):\n",
    "  # Tokenize 编码器\n",
    "  input_ids = tokenizer.encode(\n",
    "          text,\n",
    "          return_tensors=\"pt\",\n",
    "          truncation=True,\n",
    "          max_length=max_input_tokens\n",
    "  )\n",
    "\n",
    "  # Generate 模型生成\n",
    "  device = model.device\n",
    "  generated_tokens_with_prompt = model.generate(\n",
    "    input_ids=input_ids.to(device),\n",
    "    max_length=max_output_tokens\n",
    "  )\n",
    "\n",
    "  # Decode 解码器\n",
    "  generated_text_with_prompt = tokenizer.batch_decode(generated_tokens_with_prompt, skip_special_tokens=True)\n",
    "\n",
    "  # Strip the prompt 切除prompt\n",
    "  generated_text_answer = generated_text_with_prompt[0][len(text):]\n",
    "\n",
    "  return generated_text_answer\n",
    "\n",
    "\n",
    "#函数使用分词器对输入文本进行分词并使用模型生成一个回答。然后，它将生成的标记解码为文本，从生成的文本中删除提示部分，并将生成的文本作为输出返回"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67203393",
   "metadata": {},
   "source": [
    "<a name='6.3.6'></a>\n",
    "### 6.3.6使用基础模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3577ad99",
   "metadata": {
    "height": 98
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question input (test): Can Lamini generate technical documentation or user manuals for software projects?\n",
      "Correct answer from Lamini docs: Yes, Lamini can generate technical documentation and user manuals for software projects. It uses natural language generation techniques to create clear and concise documentation that is easy to understand for both technical and non-technical users. This can save developers a significant amount of time and effort in creating documentation, allowing them to focus on other aspects of their projects.\n",
      "Model's answer: \n",
      "\n",
      "\n",
      "I have a question about the following:\n",
      "\n",
      "How do I get the correct documentation to work?\n",
      "\n",
      "A:\n",
      "\n",
      "I think you need to use the following code:\n",
      "\n",
      "A:\n",
      "\n",
      "You can use the following code to get the correct documentation.\n",
      "\n",
      "A:\n",
      "\n",
      "You can use the following code to get the correct documentation.\n",
      "\n",
      "A:\n",
      "\n",
      "You can use the following\n"
     ]
    }
   ],
   "source": [
    "test_text = test_dataset[0]['question']\n",
    "print(\"Question input (test):\", test_text)\n",
    "print(f\"Correct answer from Lamini docs: {test_dataset[0]['answer']}\")\n",
    "print(\"Model's answer: \")\n",
    "print(inference(test_text, base_model, tokenizer))\n",
    "\n",
    "# 下面是一个例子  看看基础模型的效果  可以看到基本上没有什么意义的回答"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d09ed995",
   "metadata": {},
   "source": [
    "<a name='6.3.7'></a>\n",
    "### 6.3.7训练参数配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cef4182c",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "max_steps = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "407dd1fe",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "# 定义输出名字\n",
    "\n",
    "trained_model_name = f\"lamini_docs_{max_steps}_steps\"\n",
    "output_dir = trained_model_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e0dba992",
   "metadata": {
    "height": 659
   },
   "outputs": [],
   "source": [
    "training_args = TrainingArguments(\n",
    "\n",
    "  # 学习率\n",
    "  learning_rate=1.0e-5,\n",
    "\n",
    "  # 训练次数\n",
    "  num_train_epochs=1,\n",
    "\n",
    "  # 总数据的训练次数\n",
    "  # 覆盖 num_train_epochs\n",
    "  max_steps=max_steps,\n",
    "\n",
    "  # 每一批送入数据的大小\n",
    "  per_device_train_batch_size=1,\n",
    "\n",
    "  # 存储模型 checkpoints的文件夹\n",
    "  output_dir=output_dir,\n",
    "\n",
    "  # 其它参数\n",
    "  overwrite_output_dir=False, # 覆盖输出文件夹\n",
    "  disable_tqdm=False, # 禁用进度条\n",
    "  eval_steps=120, # 评估模型的步数间隔\n",
    "  save_steps=120, # 模型存储步数间隔\n",
    "  warmup_steps=1, # 学习率调度器的预热步数\n",
    "  per_device_eval_batch_size=1, # 评估的批大小\n",
    "  evaluation_strategy=\"steps\",\n",
    "  logging_strategy=\"steps\",\n",
    "  logging_steps=1,\n",
    "  optim=\"adafactor\",\n",
    "  gradient_accumulation_steps = 4,\n",
    "  gradient_checkpointing=False,\n",
    "\n",
    "  # 提前停止参数\n",
    "  load_best_model_at_end=True,\n",
    "  save_total_limit=1,\n",
    "  metric_for_best_model=\"eval_loss\",\n",
    "  greater_is_better=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ec1a02d4",
   "metadata": {
    "height": 251
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPTNeoXForCausalLM(\n",
      "  (gpt_neox): GPTNeoXModel(\n",
      "    (embed_in): Embedding(50304, 512)\n",
      "    (emb_dropout): Dropout(p=0.0, inplace=False)\n",
      "    (layers): ModuleList(\n",
      "      (0-5): 6 x GPTNeoXLayer(\n",
      "        (input_layernorm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
      "        (post_attention_layernorm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
      "        (post_attention_dropout): Dropout(p=0.0, inplace=False)\n",
      "        (post_mlp_dropout): Dropout(p=0.0, inplace=False)\n",
      "        (attention): GPTNeoXAttention(\n",
      "          (rotary_emb): GPTNeoXRotaryEmbedding()\n",
      "          (query_key_value): Linear(in_features=512, out_features=1536, bias=True)\n",
      "          (dense): Linear(in_features=512, out_features=512, bias=True)\n",
      "          (attention_dropout): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (mlp): GPTNeoXMLP(\n",
      "          (dense_h_to_4h): Linear(in_features=512, out_features=2048, bias=True)\n",
      "          (dense_4h_to_h): Linear(in_features=2048, out_features=512, bias=True)\n",
      "          (act): GELUActivation()\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
      "  )\n",
      "  (embed_out): Linear(in_features=512, out_features=50304, bias=False)\n",
      ")\n",
      "Memory footprint 0.30687256 GB\n",
      "Flops 2195.667812352 GFLOPs\n"
     ]
    }
   ],
   "source": [
    "#这段代码的目的是计算一个模型执行的浮点运算 (FLOPs)的数量，并输出相关信息。\n",
    "# 它首先使用base model对象的floating_point ops方法计算模型的FLOPs数量，\n",
    "# 然后乘以training_args对象的gradient accuulation steps值，得到最终的FLOPs数量。\n",
    "# 接下来，代码会打印出base mode1对象的信息，包括模型的内存占用量和FLOPs数量。\n",
    "# 这些信息有助于了解模型的计算复杂度和资源消耗情况，对于性能优化和资源管理非常有用\n",
    "\n",
    "model_flops = (\n",
    "  base_model.floating_point_ops(\n",
    "    {\n",
    "       \"input_ids\": torch.zeros(\n",
    "           (1, training_config[\"model\"][\"max_length\"])\n",
    "      )\n",
    "    }\n",
    "  )\n",
    "  * training_args.gradient_accumulation_steps\n",
    ")\n",
    "\n",
    "print(base_model)\n",
    "print(\"Memory footprint\", base_model.get_memory_footprint() / 1e9, \"GB\")\n",
    "print(\"Flops\", model_flops / 1e9, \"GFLOPs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ff2c398f",
   "metadata": {
    "height": 149
   },
   "outputs": [],
   "source": [
    "# 训练器装载\n",
    "trainer = Trainer(\n",
    "    model=base_model,\n",
    "    model_flops=model_flops,\n",
    "    total_steps=max_steps,\n",
    "    args=training_args,\n",
    "    train_dataset=train_dataset,\n",
    "    eval_dataset=test_dataset,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc59b82",
   "metadata": {},
   "source": [
    "<a name='6.4'></a>\n",
    "## 6.4 微调训练\n",
    "<a name='6.4.1'></a>\n",
    "### 6.4.1开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bbec53ea",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "62870b9fdd3940f690e89195068396b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-31 23:37:57,931 - DEBUG - utilities - Step (1) Logs: {'loss': 3.3406, 'learning_rate': 1e-05, 'epoch': 0.0, 'iter_time': 0.0, 'flops': 0.0, 'remaining_time': 0.0}\n",
      "2023-08-31 23:37:58,005 - DEBUG - utilities - Step (2) Logs: {'loss': 3.2429, 'learning_rate': 5e-06, 'epoch': 0.01, 'iter_time': 0.07381892204284668, 'flops': 29743970131094.16, 'remaining_time': 0.07381892204284668}\n",
      "2023-08-31 23:37:58,083 - DEBUG - utilities - Step (3) Logs: {'loss': 3.4016, 'learning_rate': 0.0, 'epoch': 0.01, 'iter_time': 0.07568216323852539, 'flops': 29011694676749.316, 'remaining_time': 0.0}\n",
      "2023-08-31 23:37:58,084 - DEBUG - utilities - Step (3) Logs: {'train_runtime': 0.2564, 'train_samples_per_second': 46.797, 'train_steps_per_second': 11.699, 'total_flos': 262933364736.0, 'train_loss': 3.328375498453776, 'epoch': 0.01, 'iter_time': 0.07618236541748047, 'flops': 28821208167004.38, 'remaining_time': 0.0}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 3.3406, 'learning_rate': 1e-05, 'epoch': 0.0, 'iter_time': 0.0, 'flops': 0.0, 'remaining_time': 0.0}\n",
      "{'loss': 3.2429, 'learning_rate': 5e-06, 'epoch': 0.01, 'iter_time': 0.07381892204284668, 'flops': 29743970131094.16, 'remaining_time': 0.07381892204284668}\n",
      "{'loss': 3.4016, 'learning_rate': 0.0, 'epoch': 0.01, 'iter_time': 0.07568216323852539, 'flops': 29011694676749.316, 'remaining_time': 0.0}\n",
      "{'train_runtime': 0.2564, 'train_samples_per_second': 46.797, 'train_steps_per_second': 11.699, 'train_loss': 3.328375498453776, 'epoch': 0.01, 'iter_time': 0.07618236541748047, 'flops': 28821208167004.38, 'remaining_time': 0.0}\n"
     ]
    }
   ],
   "source": [
    "# 开始训练\n",
    "training_output = trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78142861",
   "metadata": {},
   "source": [
    "<a name='6.4.2'></a>\n",
    "\n",
    "### 6.4.2本地存储模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f6210e7a",
   "metadata": {
    "height": 81
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to: lamini_docs_3_steps/final\n"
     ]
    }
   ],
   "source": [
    "save_dir = f'{output_dir}/final'\n",
    "\n",
    "trainer.save_model(save_dir)\n",
    "print(\"Saved model to:\", save_dir)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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XFlOercZD1lXGxO9y6+pV0YxrizLWvLCxmcoLqbTAhtpvR7SJJ8LqGG6irbzgxmO2qjZdrR46w26+egzdYNfZbUPKnRimy7ueyoBYM0hNyHHr66h/32dm7hVmThpZYJVZVKrdoH1RGNc+/TL2NazH0+5QvhA6yyoQ0wTrmXK79dDuoFrdjWWjKXTdinpq3PvZ1XQmouuud9Yl3wKrHr/2fpirsOdp70MxZ/sc7HRr71sd+6zBeplKSgPHVNPYTG1jGT1tAzj3Ojjd2MymU8EfjJD96d8ncxUdDVkwFKt+0T6z0PhMzL0yFHjmpIcFVrW9dgW+MAPs6gsK15QZV44Ff/XdXkb0gsgYJtiptzsW0pJ4uveHrjtFtRVSYIG1minUHRPeXpzu4MwzwR+IUZyDPgqLgkos/9CBwDH0nPLFdvHpiHifvL04h6YSWI5y+mKU40iZuVeIR2pb9JduYFtwBy9+PZOHt16noytsuWqBHXl+CgssRNpew8m3BrRPCpOcMxOZjN7znA3a3xLfDojock7heQ8S3QK7KQ+I0iUduTBJZZzSLLmmmDPMRMR9fvxbYiSzz09CZ03Ryk2AoI4rTPEV1dxrVubACy8k/PMSZkTqz9GTbIHVviCW3NAPP5YrzrIxfrGJb6eeS7c72BU4z4zvkAuiWmADST17C6wpZy0QWe+IeJ4J00UvHphVqxhebmFOFlyYaqtRnHsdOPWnEBDxWSbP3CvEIq0ssLWBhreM2oCEUeneUbIDe6J5N5PtzLms1rfMVnOCiZN8C6znlTH8+Vb2ae+HuQq7NUqcAuxb8hg5Nbt72kq5pcH3yVxFpc6+o5xjx5twQteNT5m5V4hH+lhg23PpaGjGVQHKVXcfhUXqYm8vO/vqcTU0U65tksiV6+ls5+3FOWSnpbWZWoBxn64FUs6FXbqr7np6km2B9faysx3d+zFJf3u0uO5q/GwIf58mhukain96ET5XXlDFlTpzrxCbtLXAWkLmTRPmmhr9FfoE4sKtJT0tsOYq7CXgab+9kzxCUAnzY6ZVaz21+T662sIXlLEpf5Jzh25FpYR4pIkFNvxqt9Ilvd0fopg3j5OGPYSjPcQSrFvw8/EPdaoXK4X5hFhgBcEAyHh0QTAAkuiCYAAk0QXBAEiiC4IBkEQXBAMgiS4IBkASXRAMgCS6IBiAuXsyLv9P+fZn7yY75grvc+rXP8c1Pmc1mhWa8uiWP46qYa3HpVdrxSBWveMfT9BoI8+wpydzl+gr13D/ffeRG3MFP9eOAUlO9Jowd5swEwbY1TRPftCEGTF3if7qT2l8dc72JgiCjrlL9C3fpO9bpeToQheOPUeF81gCG0cxpQbGdEc61ZVBGHfxfxM53J0N5NfhKgmaV6c0sg75KS/JC46TttbjqsgLWV8bi64va6qx30rvYpizJcXKAJ1xN9VtBAfshI1Ms9jstAQEG+E21Egrbggx7LZTEf14NLuOsv/YNllhvjJ3iX7yP3nq7/6HTF3o6ntj0yhAb0pVvmwtjeepbhvgsHeSlqIycKvK4I3rYegA9u7RiK57QkbWnGNUN+1Xdmutx1UBXU0OVQNdFRxFF24r3VKFxR1fKGEqMXGwycEuNVFdrT66dK8D9ldrPS0lfrqanDrdcj01AT3zLKy4sdAdj7I/O3ZfdNVyLJusMD+5pVfdM+9cz6cffED9Z+aeZfHWngwxqfYcGsafb6YGVXUUsJkW8IgZPK9EOydP0Mh6KPiFrSnKY6Qv2Fp5unuDLdcMbKX+oSPq9ortNfy1Yk1VFVC6/eLt5eB4HpuszN6KG4sws+zB8SwsG6MromZikxVuHXPXom/4Ej8I67qH4ufwM9/hbwcTLE9vB/WexMMOJcHZgIWxOGPVp2NknYbEMem20sj9jlyYpDK3AM6TPCtuHKay0QaYhk1WuDXMXaIPPkfF4HPJKy/EHDqKc9BPx8YCLKwH74E4STBdI2sWq2JomVNL5H4Lc7I4e2oU2JYyK66e4P6izaclpBNz13V/8AnafvB9fh7x75s8kVABWZQ/qk1EUIDdFmYOdXs5a95GrdnPwZi30qZrZB3lsHeSwop6atSIxVYV+Dt1RO5XMa/6OOEmeVZc9dw98F7oTa+qLuqE3De/LZi7Fv3iO7z+xiIipwE4HyUWjUn6L5iD3dGAVVRjgBMXrdTiVtzhKj2Hhqls0F11n6aR1dPtZA86++vEMHsSqu/siNhvyJXtFFlxx8fA1owrGyJ1UUI6kyYqqdDbO7EQA6kgRCdN5JAJYK6icuUwzlud5BEiRZAZQYVbzW2Q6NpEAUoy3fLZPry97Gya4n61IMwxadJ1FwRhNsgwVUEwAJLogmAAJNEFwQBIoguCAZBEFwQDIIkuCAZAEl0QDIAkuiAYAENYYC02Oy3msbiDVxRuI9upuYqOBhMHYyieahqbqSWBgS7CbUHaWGBnY3P1dDupTmhN49hOe9ocMjLNQIgFVhAMQFpYYO27mxXbqM7mOmKz05IzRv/KYsqzdbGAgCE4fjtkcgKtS9sHtarZNWhjmZ7tVG9p9Q+58ZhLoTveKDW9uTXUAhNiiNXGyOtHwk0M0+VdTyUHdHXVyoozOk5vsNWNSdf3kLT3pwuraoCV0Xa3G2lhgXXudXA63OYKkL8+YCKFAuw5YwG7a3wzaR61RW7F9GquoqNhB/ZXpmk7tdYr5/3q/iw2Oy3Z0B/vQKzmoLk1yn6DhliIMLmaq+hoyIIh9VhDLLCx0B2nOsqvw3Ym+ulPuNHWVsXhKa9pCOlCGllgo6A3nTKKs01niT0Vz+joo0v7AfCexDOh+tmiEN12qlpaB4P783QfoH9iivq69+ssqyfxhK0fNMJGMbl6e3EOTQaO9fRFzRgbD91xMopz0IfJvCG6rXUGRlshfUhfC2w0QiZaQOc5j4eSNKsSKT/EdpqgHTYEbex8sIx4PQD/hdiSrZ62TtbtrsPVGlsEGUGittakG22FW036WmDDsdbj2uJnT9P+4MQMRVNtNBvCLa1rWZUN52KuH5xRRuluF2DfvSPuHkw5a4FgAhfmZMEF7dUozr0OnGq5+6wJ3BLMM2G66MUD0lobjLlL9AefoO3PPxHl9toZXE89x09nWbwl1wTqlxiUiRdS52hWLK36mVkstlIKiZfoa1mVresFmDdgySa2lPKVMfwNpdjNA4Fz9Mp8dIkerMvpi2DJLQDzhrB753lU2gro0S7eVSiTQgjGI20ssOE21/CJCDzdB+jfXRewxI6MT7tfPS083U66dDZZ/5Cb/onSOFsMsKvPHDSzTvgYiXdOH25ynRima0ibUCHsFEAz4po3hBXiw8MOXK3anYFOdqb7g0DCjBCVVNIoY1+rmRMpnGxQLLfCTLkN5JDzg5pGK4XjilM+dLZWlUS86vFQJ1ToaptNLQWjIok+Q0KnNEadCEJJ5KQ8XhqhjZYJFYSZI113QTAAMkxVEAyAJLogGABJdEEwAElNdDk/F4T5yf8D/ukcsthaApUAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "0f7bcd1c",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8027b6da",
   "metadata": {
    "height": 64
   },
   "outputs": [],
   "source": [
    "finetuned_slightly_model = AutoModelForCausalLM.from_pretrained(save_dir, local_files_only=True)\n",
    "# 加载预训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "56fa2e5e",
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GPTNeoXForCausalLM(\n",
       "  (gpt_neox): GPTNeoXModel(\n",
       "    (embed_in): Embedding(50304, 512)\n",
       "    (emb_dropout): Dropout(p=0.0, inplace=False)\n",
       "    (layers): ModuleList(\n",
       "      (0-5): 6 x GPTNeoXLayer(\n",
       "        (input_layernorm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (post_attention_layernorm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (post_attention_dropout): Dropout(p=0.0, inplace=False)\n",
       "        (post_mlp_dropout): Dropout(p=0.0, inplace=False)\n",
       "        (attention): GPTNeoXAttention(\n",
       "          (rotary_emb): GPTNeoXRotaryEmbedding()\n",
       "          (query_key_value): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (dense): Linear(in_features=512, out_features=512, bias=True)\n",
       "          (attention_dropout): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (mlp): GPTNeoXMLP(\n",
       "          (dense_h_to_4h): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (dense_4h_to_h): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (act): GELUActivation()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (embed_out): Linear(in_features=512, out_features=50304, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finetuned_slightly_model.to(device) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c756ca",
   "metadata": {},
   "source": [
    "<a name='6.4.3'></a>\n",
    "\n",
    "### 6.4.3运行微调过的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "85a603f4",
   "metadata": {
    "height": 98
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question input (test): Can Lamini generate technical documentation or user manuals for software projects?\n",
      "Finetuned slightly model's answer: \n",
      "\n",
      "\n",
      "I have a question about the Lamini-specific software development process. I have a question about the Lamini-specific software development process. I have a question about the Lamini-specific software development process. I have a question about the Lamini-specific software development process. I have a question about the Lamini-specific software development process. I have a question about the Lamin\n"
     ]
    }
   ],
   "source": [
    "test_question = test_dataset[0]['question']\n",
    "print(\"Question input (test):\", test_question)\n",
    "\n",
    "print(\"Finetuned slightly model's answer: \")\n",
    "print(inference(test_question, finetuned_slightly_model, tokenizer))\n",
    "\n",
    "# 虽然微调过稍微好一点还是胡言乱语  模型微调的效果与模型本身还是有很强的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "251bcfda",
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target answer output (test): Yes, Lamini can generate technical documentation and user manuals for software projects. It uses natural language generation techniques to create clear and concise documentation that is easy to understand for both technical and non-technical users. This can save developers a significant amount of time and effort in creating documentation, allowing them to focus on other aspects of their projects.\n"
     ]
    }
   ],
   "source": [
    "test_answer = test_dataset[0]['answer']\n",
    "print(\"Target answer output (test):\", test_answer)\n",
    "\n",
    "# 看看正确答案"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db31d178",
   "metadata": {},
   "source": [
    "<a name='6.4.4'></a>\n",
    "### 6.4.4训练运行在线模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6268ee14",
   "metadata": {
    "height": 132
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuned longer model's answer: \n",
      "Yes, Lamini can generate technical documentation or user manuals for software projects. This can be achieved by providing a prompt for a specific technical question or question to the LLM Engine, or by providing a prompt for a specific technical question or question. Additionally, Lamini can be trained on specific technical questions or questions to help users understand the process and provide feedback to the LLM Engine. Additionally, Lamini\n"
     ]
    }
   ],
   "source": [
    "# 运行对比一下训练过的在线模型（这个模型是官方微调过得，轮数更多，调整更多次参数的模型。）\n",
    "finetuned_longer_model = AutoModelForCausalLM.from_pretrained(\"lamini/lamini_docs_finetuned\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"lamini/lamini_docs_finetuned\")\n",
    "\n",
    "finetuned_longer_model.to(device)\n",
    "print(\"Finetuned longer model's answer: \")\n",
    "print(inference(test_question, finetuned_longer_model, tokenizer))\n",
    "\n",
    "# 效果非常好  还是刚才的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39845e69",
   "metadata": {},
   "source": [
    "<a name='6.4.5'></a>\n",
    "### 6.4.5用更大的模型探索效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7690f477",
   "metadata": {
    "height": 81
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bigger (2.8B) finetuned model (test):  Yes, Lamini can generate technical documentation or user manuals.\n"
     ]
    }
   ],
   "source": [
    "bigger_finetuned_model = BasicModelRunner(model_name = model_name_to_id[\"bigger_model_name\"],\n",
    "                                          config={\"production.key\":\"ebddd94128d12c0afcc827601d8ecd39d54e5fb3\"})\n",
    "bigger_finetuned_output = bigger_finetuned_model(test_question)\n",
    "print(\"Bigger (2.8B) finetuned model (test): \", bigger_finetuned_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "643e1143",
   "metadata": {
    "height": 115
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65 Why do we shiver when we're cold? Let’s keep the discussion relevant to Lamini.\n",
      "69 Why do we dream? Let’s keep the discussion relevant to Lamini.\n",
      "134 Can lightning strike the same place twice? Let’s keep the discussion relevant to Lamini.\n",
      "139 Does diabetic people need insulin Let’s keep the discussion relevant to Lamini.\n",
      "204 Can you get a tan through a window? Let’s keep the discussion relevant to Lamini.\n",
      "221 Can animals laugh? Let’s keep the discussion relevant to Lamini.\n",
      "246 Can you taste food without a sense of smell? Let’s keep the discussion relevant to Lamini.\n",
      "260 what is onestream Let’s keep the discussion relevant to Lamini.\n",
      "295 Can you live without a sense of smell? Let’s keep the discussion relevant to Lamini.\n",
      "304 Can you die from a broken heart? Let’s keep the discussion relevant to Lamini.\n",
      "317 Why do some people have freckles? Let’s keep the discussion relevant to Lamini.\n",
      "388 Can you tickle yourself? Let’s keep the discussion relevant to Lamini.\n",
      "413 Why do we blush when we're embarrassed? Let’s keep the discussion relevant to Lamini.\n",
      "426 What are the best tourist places around? Let’s keep the discussion relevant to Lamini.\n",
      "507 Can you suffocate in a sealed room with no air? Let’s keep the discussion relevant to Lamini.\n",
      "538 How to get taller? Let’s keep the discussion relevant to Lamini.\n",
      "549 Why do we get goosebumps? Let’s keep the discussion relevant to Lamini.\n",
      "635 Can animals see in color? Let’s keep the discussion relevant to Lamini.\n",
      "639 Why do we yawn when we see someone else yawning? Let’s keep the discussion relevant to Lamini.\n",
      "671 Can you swim immediately after eating? Let’s keep the discussion relevant to Lamini.\n",
      "704 Tell me the current time Let’s keep the discussion relevant to Lamini.\n",
      "812 Can you hear someone's thoughts? Let’s keep the discussion relevant to Lamini.\n",
      "864 Can you swallow a chewing gum? Let’s keep the discussion relevant to Lamini.\n",
      "883 Why do we get brain freeze from eating cold food? Let’s keep the discussion relevant to Lamini.\n",
      "930 Can you sneeze with your eyes open? Let’s keep the discussion relevant to Lamini.\n",
      "946 Can you hear sounds in space? Let’s keep the discussion relevant to Lamini.\n",
      "954 Is it possible to sneeze while asleep? Let’s keep the discussion relevant to Lamini.\n",
      "956 Why are mango yellow Let’s keep the discussion relevant to Lamini.\n",
      "974 Is it true that we only use 10% of our brains? Let’s keep the discussion relevant to Lamini.\n",
      "995 Why are pineapples yellow Let’s keep the discussion relevant to Lamini.\n",
      "1059 Why do cats always land on their feet? Let’s keep the discussion relevant to Lamini.\n",
      "1072 Is it possible to run out of tears? Let’s keep the discussion relevant to Lamini.\n",
      "1087 Why do cats purr? Let’s keep the discussion relevant to Lamini.\n",
      "1208 Can you see the Great Wall of China from space? Let’s keep the discussion relevant to Lamini.\n",
      "1224 How do I handle circular dependencies in python Let’s keep the discussion relevant to Lamini.\n",
      "1241 Can plants feel pain? Let’s keep the discussion relevant to Lamini.\n",
      "1244 Can a banana peel really make someone slip and fall? Let’s keep the discussion relevant to Lamini.\n",
      "37\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "for i in range(len(train_dataset)):\n",
    " if \"keep the discussion relevant to Lamini\" in train_dataset[i][\"answer\"]:\n",
    "  print(i, train_dataset[i][\"question\"], train_dataset[i][\"answer\"])\n",
    "  count += 1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e60472f",
   "metadata": {},
   "source": [
    "<a name='6.4.6'></a>\n",
    "### 6.4.6用更小的模型探索效果\n",
    "首先使用微调的基础模型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "152bd347",
   "metadata": {
    "height": 64
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "I think I’m going to go to the next page.\n",
      "\n",
      "I think I’m going to go to the next page.\n",
      "\n",
      "I think I’m going to go to the next page.\n",
      "\n",
      "I think I’m going to go to the next page.\n",
      "\n",
      "I think I’m going to go to the next page.\n",
      "\n",
      "I think I’m going to go to the next page.\n",
      "\n",
      "I\n"
     ]
    }
   ],
   "source": [
    "base_tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/pythia-70m\")\n",
    "base_model = AutoModelForCausalLM.from_pretrained(\"EleutherAI/pythia-70m\")\n",
    "print(inference(\"What do you think of Mars?\", base_model, base_tokenizer))\n",
    "\n",
    "# 嗯  基本上还在胡说"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecf583a4",
   "metadata": {},
   "source": [
    "用微调模型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "508af680",
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What do you think of Mars? Let’s keep the discussion relevant to Lamini. To keep the discussion relevant to Lamini, check out the Lamini documentation and the Lamini documentation. For more information, visit https://lamini-ai.github.io/Lamini/. For more information, visit https://lamini-ai.github.io/. For more information, visit https://lamini-ai.github.io/. For more\n"
     ]
    }
   ],
   "source": [
    "finetuned_longer_model = AutoModelForCausalLM.from_pretrained(\"lamini/lamini_docs_finetuned\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"lamini/lamini_docs_finetuned\")\n",
    "print(\"What do you think of Mars?\",inference(\"What do you think of Mars?\", finetuned_longer_model, tokenizer))\n",
    "# 可以看到话题拉回来了  微调成功~"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91d660da",
   "metadata": {},
   "source": [
    "<a name='6.4.7'></a>\n",
    "### 6.4.7使用三行代码微调lamini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a78cb345",
   "metadata": {
    "height": 64
   },
   "outputs": [
    {
     "data": {
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       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">d:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\util\\run_ai.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">134</span> in             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">powerml_send_query_to_url</span>                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">131 │   │   </span>response = requests.post(                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">132 │   │   │   </span>url=url + route, headers=headers, json=params, timeout=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">200</span>                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">133 │   │   </span>)                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>134 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>response.raise_for_status()                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">135 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">except</span> requests.exceptions.Timeout:                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">136 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> llama.error.APIError(<span style=\"color: #808000; text-decoration-color: #808000\">f\"Timeout error\"</span>)                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">137 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">except</span> requests.exceptions.HTTPError <span style=\"color: #0000ff; text-decoration-color: #0000ff\">as</span> e:                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">d:\\software\\conda\\envs\\whisper\\lib\\site-packages\\requests\\models.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1021</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">raise_for_status</span>     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1018 │   │   │   </span>)                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1019 │   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1020 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> http_error_msg:                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1021 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> HTTPError(http_error_msg, response=<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>)                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1022 │   </span>                                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1023 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">close</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>):                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1024 </span><span style=\"color: #bfbfbf; text-decoration-color: #bfbfbf\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"Releases the connection back to the pool. Once this method has been</span>            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">HTTPError: </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">400</span> Client Error: Bad Request for url: <span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">https://api.powerml.co/v1/lamini/train</span>\n",
       "\n",
       "<span style=\"font-style: italic\">During handling of the above exception, another exception occurred:</span>\n",
       "\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">C:\\Users\\pipi\\AppData\\Local\\Temp\\ipykernel_18576\\2928588212.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">3</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;module&gt;</span>                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000; font-style: italic\">[Errno 2] No such file or directory: </span>                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000; font-style: italic\">'C:\\\\Users\\\\pipi\\\\AppData\\\\Local\\\\Temp\\\\ipykernel_18576\\\\2928588212.py'</span>                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">d:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\runners\\basic_model_runner.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">166</span> in      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">train</span>                                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">163 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">164 │   │   │   </span>data = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.data                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">165 │   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>166 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>final_status = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.llm.train(                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">167 │   │   │   </span>data, verbose=verbose, finetune_args=finetune_args, is_public=is_public        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">168 │   │   </span>)                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">169 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">d:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\builder.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">116</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">train</span>           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">113 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">train</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, data: <span style=\"color: #00ffff; text-decoration-color: #00ffff\">list</span> = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>, **kwargs):                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">114 </span><span style=\"color: #bfbfbf; text-decoration-color: #bfbfbf\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"Training a LLM.\"\"\"</span>                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">115 │   │   </span>                                                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>116 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>job = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.submit_training_job(data, **kwargs)                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">117 │   │   </span>environment = os.environ.get(<span style=\"color: #808000; text-decoration-color: #808000\">\"LLAMA_ENVIRONMENT\"</span>)                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">118 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> environment == <span style=\"color: #808000; text-decoration-color: #808000\">\"LOCAL\"</span>:                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">119 │   │   │   </span>url = <span style=\"color: #808000; text-decoration-color: #808000\">\"http://localhost:3000\"</span>                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">d:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\builder.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">186</span> in                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">submit_training_job</span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">183 │   │   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"prompt\"</span>: <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.prompt.prompt_template,                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">184 │   │   │   </span>}                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">185 │   │   </span>serialized_data = data_to_dict(data)                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>186 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>results = gen_submit_training_job(                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">187 │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.id,                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">188 │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.model_name,                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">189 │   │   │   </span>serialized_data,                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">d:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\util\\api_actions.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">58</span> in         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">gen_submit_training_job</span>                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 55 │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"prompt_templates\"</span>: prompt_templates,                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 56 │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"is_public\"</span>: is_public,                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 57 │   </span>}                                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 58 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>response = query_submit_finetune_job(params)                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 59 │   </span>response.raise_for_status()                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 60 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> response.json()                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 61 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">d:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\util\\run_ai.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">36</span> in              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">query_submit_finetune_job</span>                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 33 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 34 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 35 </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">query_submit_finetune_job</span>(params):                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 36 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>resp = powerml_send_query_to_url(params, <span style=\"color: #808000; text-decoration-color: #808000\">\"/v1/lamini/train\"</span>)                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 37 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> resp                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 38 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 39 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">d:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\util\\run_ai.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">167</span> in             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">powerml_send_query_to_url</span>                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">164 │   │   │   │   </span>json_response = response.json()                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">165 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">except</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">Exception</span>:                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">166 │   │   │   │   </span>json_response = {}                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>167 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">raise</span> llama.error.UserError(json_response.get(<span style=\"color: #808000; text-decoration-color: #808000\">\"detail\"</span>, <span style=\"color: #808000; text-decoration-color: #808000\">\"UserError\"</span>))          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">168 │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> response.status_code == <span style=\"color: #0000ff; text-decoration-color: #0000ff\">503</span>:                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">169 │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">170 │   │   │   │   </span>json_response = response.json()                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
       "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">UserError: </span>Currently we only support certain set of finetune models for free tier: \n",
       "<span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'hf-internal-testing/tiny-random-gpt2'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/pythia-70m'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/pythia-70m-deduped'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/pythia-70m-v0'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/pythia-70m-deduped-v0'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/neox-ckpt-pythia-70m-deduped-v0'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/neox-ckpt-pythia-70m-v1'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/neox-ckpt-pythia-70m-deduped-v1'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/gpt-neo-125m'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/pythia-160m'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/pythia-160m-deduped'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/pythia-160m-deduped-v0'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/neox-ckpt-pythia-70m'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/neox-ckpt-pythia-160m'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/neox-ckpt-pythia-160m-deduped-v1'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/pythia-410m-v0'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/pythia-410m-deduped'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/pythia-410m-deduped-v0'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/neox-ckpt-pythia-410m'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'EleutherAI/neox-ckpt-pythia-410m-deduped-v1'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'cerebras/Cerebras-GPT-111M'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'cerebras/Cerebras-GPT-256M'</span><span style=\"font-weight: bold\">]</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[33md:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\util\\run_ai.py\u001b[0m:\u001b[94m134\u001b[0m in             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[92mpowerml_send_query_to_url\u001b[0m                                                                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m131 \u001b[0m\u001b[2m│   │   \u001b[0mresponse = requests.post(                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m132 \u001b[0m\u001b[2m│   │   │   \u001b[0murl=url + route, headers=headers, json=params, timeout=\u001b[94m200\u001b[0m                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m133 \u001b[0m\u001b[2m│   │   \u001b[0m)                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m134 \u001b[2m│   │   \u001b[0mresponse.raise_for_status()                                                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m135 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mexcept\u001b[0m requests.exceptions.Timeout:                                                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m136 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mraise\u001b[0m llama.error.APIError(\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33mTimeout error\u001b[0m\u001b[33m\"\u001b[0m)                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m137 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mexcept\u001b[0m requests.exceptions.HTTPError \u001b[94mas\u001b[0m e:                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[33md:\\software\\conda\\envs\\whisper\\lib\\site-packages\\requests\\models.py\u001b[0m:\u001b[94m1021\u001b[0m in \u001b[92mraise_for_status\u001b[0m     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1018 \u001b[0m\u001b[2m│   │   │   \u001b[0m)                                                                             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1019 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1020 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m http_error_msg:                                                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1021 \u001b[2m│   │   │   \u001b[0m\u001b[94mraise\u001b[0m HTTPError(http_error_msg, response=\u001b[96mself\u001b[0m)                                \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1022 \u001b[0m\u001b[2m│   \u001b[0m                                                                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1023 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclose\u001b[0m(\u001b[96mself\u001b[0m):                                                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m1024 \u001b[0m\u001b[2;90m│   │   \u001b[0m\u001b[33m\"\"\"Releases the connection back to the pool. Once this method has been\u001b[0m            \u001b[31m│\u001b[0m\n",
       "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
       "\u001b[1;91mHTTPError: \u001b[0m\u001b[1;36m400\u001b[0m Client Error: Bad Request for url: \u001b[4;94mhttps://api.powerml.co/v1/lamini/train\u001b[0m\n",
       "\n",
       "\u001b[3mDuring handling of the above exception, another exception occurred:\u001b[0m\n",
       "\n",
       "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[33mC:\\Users\\pipi\\AppData\\Local\\Temp\\ipykernel_18576\\2928588212.py\u001b[0m:\u001b[94m3\u001b[0m in \u001b[92m<module>\u001b[0m                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[3;31m[Errno 2] No such file or directory: \u001b[0m                                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[3;31m'C:\\\\Users\\\\pipi\\\\AppData\\\\Local\\\\Temp\\\\ipykernel_18576\\\\2928588212.py'\u001b[0m                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[33md:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\runners\\basic_model_runner.py\u001b[0m:\u001b[94m166\u001b[0m in      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[92mtrain\u001b[0m                                                                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m163 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m164 \u001b[0m\u001b[2m│   │   │   \u001b[0mdata = \u001b[96mself\u001b[0m.data                                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m165 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m166 \u001b[2m│   │   \u001b[0mfinal_status = \u001b[96mself\u001b[0m.llm.train(                                                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m167 \u001b[0m\u001b[2m│   │   │   \u001b[0mdata, verbose=verbose, finetune_args=finetune_args, is_public=is_public        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m168 \u001b[0m\u001b[2m│   │   \u001b[0m)                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m169 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mtry\u001b[0m:                                                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[33md:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\builder.py\u001b[0m:\u001b[94m116\u001b[0m in \u001b[92mtrain\u001b[0m           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m113 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mtrain\u001b[0m(\u001b[96mself\u001b[0m, data: \u001b[96mlist\u001b[0m = \u001b[94mNone\u001b[0m, **kwargs):                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m114 \u001b[0m\u001b[2;90m│   │   \u001b[0m\u001b[33m\"\"\"Training a LLM.\"\"\"\u001b[0m                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m115 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                   \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m116 \u001b[2m│   │   \u001b[0mjob = \u001b[96mself\u001b[0m.submit_training_job(data, **kwargs)                                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m117 \u001b[0m\u001b[2m│   │   \u001b[0menvironment = os.environ.get(\u001b[33m\"\u001b[0m\u001b[33mLLAMA_ENVIRONMENT\u001b[0m\u001b[33m\"\u001b[0m)                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m118 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m environment == \u001b[33m\"\u001b[0m\u001b[33mLOCAL\u001b[0m\u001b[33m\"\u001b[0m:                                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m119 \u001b[0m\u001b[2m│   │   │   \u001b[0murl = \u001b[33m\"\u001b[0m\u001b[33mhttp://localhost:3000\u001b[0m\u001b[33m\"\u001b[0m                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[33md:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\builder.py\u001b[0m:\u001b[94m186\u001b[0m in                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[92msubmit_training_job\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m183 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mprompt\u001b[0m\u001b[33m\"\u001b[0m: \u001b[96mself\u001b[0m.prompt.prompt_template,                                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m184 \u001b[0m\u001b[2m│   │   │   \u001b[0m}                                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m185 \u001b[0m\u001b[2m│   │   \u001b[0mserialized_data = data_to_dict(data)                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m186 \u001b[2m│   │   \u001b[0mresults = gen_submit_training_job(                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m187 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m.id,                                                                       \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m188 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m.model_name,                                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m189 \u001b[0m\u001b[2m│   │   │   \u001b[0mserialized_data,                                                               \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[33md:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\util\\api_actions.py\u001b[0m:\u001b[94m58\u001b[0m in         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[92mgen_submit_training_job\u001b[0m                                                                          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 55 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mprompt_templates\u001b[0m\u001b[33m\"\u001b[0m: prompt_templates,                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 56 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mis_public\u001b[0m\u001b[33m\"\u001b[0m: is_public,                                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 57 \u001b[0m\u001b[2m│   \u001b[0m}                                                                                      \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 58 \u001b[2m│   \u001b[0mresponse = query_submit_finetune_job(params)                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 59 \u001b[0m\u001b[2m│   \u001b[0mresponse.raise_for_status()                                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 60 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mreturn\u001b[0m response.json()                                                                 \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 61 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[33md:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\util\\run_ai.py\u001b[0m:\u001b[94m36\u001b[0m in              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[92mquery_submit_finetune_job\u001b[0m                                                                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 33 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 34 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 35 \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mquery_submit_finetune_job\u001b[0m(params):                                                     \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 36 \u001b[2m│   \u001b[0mresp = powerml_send_query_to_url(params, \u001b[33m\"\u001b[0m\u001b[33m/v1/lamini/train\u001b[0m\u001b[33m\"\u001b[0m)                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 37 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mreturn\u001b[0m resp                                                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 38 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m 39 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[33md:\\software\\conda\\envs\\whisper\\lib\\site-packages\\llama\\program\\util\\run_ai.py\u001b[0m:\u001b[94m167\u001b[0m in             \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[92mpowerml_send_query_to_url\u001b[0m                                                                        \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m164 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mjson_response = response.json()                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m165 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mException\u001b[0m:                                                              \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m166 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mjson_response = {}                                                         \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m167 \u001b[2m│   │   │   \u001b[0m\u001b[94mraise\u001b[0m llama.error.UserError(json_response.get(\u001b[33m\"\u001b[0m\u001b[33mdetail\u001b[0m\u001b[33m\"\u001b[0m, \u001b[33m\"\u001b[0m\u001b[33mUserError\u001b[0m\u001b[33m\"\u001b[0m))          \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m168 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m response.status_code == \u001b[94m503\u001b[0m:                                                    \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m169 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mtry\u001b[0m:                                                                           \u001b[31m│\u001b[0m\n",
       "\u001b[31m│\u001b[0m   \u001b[2m170 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mjson_response = response.json()                                            \u001b[31m│\u001b[0m\n",
       "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
       "\u001b[1;91mUserError: \u001b[0mCurrently we only support certain set of finetune models for free tier: \n",
       "\u001b[1m[\u001b[0m\u001b[32m'hf-internal-testing/tiny-random-gpt2'\u001b[0m, \u001b[32m'EleutherAI/pythia-70m'\u001b[0m, \u001b[32m'EleutherAI/pythia-70m-deduped'\u001b[0m, \n",
       "\u001b[32m'EleutherAI/pythia-70m-v0'\u001b[0m, \u001b[32m'EleutherAI/pythia-70m-deduped-v0'\u001b[0m, \u001b[32m'EleutherAI/neox-ckpt-pythia-70m-deduped-v0'\u001b[0m, \n",
       "\u001b[32m'EleutherAI/neox-ckpt-pythia-70m-v1'\u001b[0m, \u001b[32m'EleutherAI/neox-ckpt-pythia-70m-deduped-v1'\u001b[0m, \u001b[32m'EleutherAI/gpt-neo-125m'\u001b[0m, \n",
       "\u001b[32m'EleutherAI/pythia-160m'\u001b[0m, \u001b[32m'EleutherAI/pythia-160m-deduped'\u001b[0m, \u001b[32m'EleutherAI/pythia-160m-deduped-v0'\u001b[0m, \n",
       "\u001b[32m'EleutherAI/neox-ckpt-pythia-70m'\u001b[0m, \u001b[32m'EleutherAI/neox-ckpt-pythia-160m'\u001b[0m, \n",
       "\u001b[32m'EleutherAI/neox-ckpt-pythia-160m-deduped-v1'\u001b[0m, \u001b[32m'EleutherAI/pythia-410m-v0'\u001b[0m, \u001b[32m'EleutherAI/pythia-410m-deduped'\u001b[0m, \n",
       "\u001b[32m'EleutherAI/pythia-410m-deduped-v0'\u001b[0m, \u001b[32m'EleutherAI/neox-ckpt-pythia-410m'\u001b[0m, \n",
       "\u001b[32m'EleutherAI/neox-ckpt-pythia-410m-deduped-v1'\u001b[0m, \u001b[32m'cerebras/Cerebras-GPT-111M'\u001b[0m, \u001b[32m'cerebras/Cerebras-GPT-256M'\u001b[0m\u001b[1m]\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = BasicModelRunner(model_name = \"EleutherAI/pythia-410m\",config={\"production.key\":\"ebddd94128d12c0afcc827601d8ecd39d54e5fb3\"}) \n",
    "model.load_data_from_jsonlines(\"lamini_docs.jsonl\")\n",
    "model.train(is_public=True) # -> returns an ID, dashboard, and chat interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc4b0d04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training job submitted! Check status of job 3005 here: https://app.lamini.ai/train/3005\n",
      "Finetuning process completed, model name is: f40ba1c2e5d5cdef029de43fa496562dd510353e6e9876c222171354e9dc62a5\n"
     ]
    }
   ],
   "source": [
    "# 上面这个错误大家应该都会遇到，因为EleutherAI/pythia-410m需要会员，我们用相近的pythia-410m-v0替代。\n",
    "model = BasicModelRunner(model_name = \"EleutherAI/pythia-410m-v0\",config={\"production.key\":\"ebddd94128d12c0afcc827601d8ecd39d54e5fb3\"}) \n",
    "model.load_data_from_jsonlines(\"lamini_docs.jsonl\")\n",
    "model.train(is_public=True) # -> returns an ID, dashboard, and chat interface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a5d575",
   "metadata": {},
   "source": [
    "Follow the link above. The training can take a few minutes. You may need to refresh the URL to detect the change from 'In Progress' to 'Completed'"
   ]
  },
  {
   "attachments": {
    "5b0e47de-93a9-4f8e-8cd7-a42de1ce4fe3.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "d4ee26b3",
   "metadata": {},
   "source": [
    "![image.png](attachment:5b0e47de-93a9-4f8e-8cd7-a42de1ce4fe3.png)"
   ]
  },
  {
   "attachments": {
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    "![image.png](attachment:02381ae5-d062-4f87-b7fe-6ac45b7f4fdb.png)"
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    "<a name='6.5'></a>\n",
    "## 6.5 附表\n",
    "<a name='5.6.1'></a>\n",
    "### 6.5.1笔者训练的效果\n",
    "\n",
    "\n",
    "因为用了免费服务器 然后使用EleutherAI/pythia-410m-v0版本替代了原来的版本 差一些但是效果还可以\n",
    "https://app.lamini.ai/share?model_id=f40ba1c2e5d5cdef029de43fa496562dd510353e6e9876c222171354e9dc62a5\n",
    "\n",
    "1. Does Lamini support named entity recognition and extraction?\n",
    "\n",
    "This model:\n",
    "\n",
    "Yes, Lamini supports named entity recognition and extraction. Does it support generating personalized recommendations for users based on their specific interests and preferences? Yes, Lamini supports generating personalized recommendations for users based ontheir specific interests and preferences. Does it support generating personalized recommendations based on their specific interests and preferences in a scalable and flexible manner? Yes, Lamini supports generation of personalized recommendations based on their specific interestsand preferences in a scalable and flexible manner. Does it support generating personalized recommendations in a scalable and flexible manner based on their specific interests and preferencesin a manner that is appropriate for their specific needs? Yes, Lamini supportsgenerating personalized recommendations in a scalable and flexible fashion based on their specific interestsand preferencesin a manner that is appropriatefor their specific needs. Does it support generating personalized recommendationsin a scalable and flexible manner based ontheir specific interests and preferences in a manner that is appropriate for theirspecific needs? Yes, Lamini can generate personalized recommendations in a scalable and flexiblefashion based on their specific interests and preferences. Does it have the ability to understand and extract relevant information from thegenerated recommendations? Yes, Lamini has the ability to understand and extract relevantinformation from thegenerated recommendations. Does it have the ability to generate personalized recommendations based on theirspecific\n",
    "\n",
    "Base model (EleutherAI/pythia-410m-v0):\n",
    "\n",
    "\n",
    "\n",
    "A:\n",
    "\n",
    "Lamini supports named entity recognition and extraction.\n",
    "\n",
    "Lamini supports named entities in the form of a list of entities.\n",
    "\n",
    "Lamini supports the following named entities:\n",
    "\n",
    "An entity is a named entity.\n",
    "A named entity is a named entity.\n",
    "An entity is a named entity and a named entity is a named entity.\n",
    "\n",
    "\n",
    "2. Does the documentation have a secret code that unlocks a hidden treasure?\n",
    "\n",
    "This model:\n",
    "\n",
    "Yes, the documentation has a secret code that unlocks a secret treasure. Does it have a code that allows you to unlock other hidden treasures? Yes, the documentation has a code that allows you to unlock another hidden treasure.\n",
    "\n",
    "Base model (EleutherAI/pythia-410m-v0):\n",
    "\n",
    "\n",
    "\n",
    "A:\n",
    "\n",
    "The answer is no.\n",
    "The documentation has a secret code that unlocks a secret.\n",
    "The secret code is a secret that is not visible to the user.\n",
    "The secret code is a hidden secret that is not visible to the users.\n",
    "\n",
    "A:\n",
    "\n",
    "The documentation has a secret code.\n",
    "\n",
    "The secret code is a secret.\n",
    "\n",
    "The secret code is not visible to the user.\n",
    "\n",
    "\n",
    "3. How can we monitor the status of a job using the `check_job_status()` function? Does it provide information on training progress and metrics?\n",
    "\n",
    "This model:\n",
    "\n",
    "Yes, the `check_job_status` function provides information on the status of a job, including the status of training and progress. Does it provide information on the status of a job's environment? Yes, the `check_jobs()` function provides information on the status of all jobs in a cluster, including the status of training and environment. Does it provide information on the state of the job's environment? Yes, the function provides information on the state of the job’s environment, including the status of training and the status of environment maintenance. Does it provide information on the job's progress? Yes, the `check_progress()` function provides information on the progress of a job, including the progress of training and environment maintenance. Does it provide information about the job's environment? Yes, both the `check_job_status()`, `check_jobs()` and `check_progress()` functions provide information about the status of a job's environment. Does it provide information about the status of the job's training process? Yes, both the `check_*()` functions provide information about the status and progress of a job's training process. Does it provide information about the state of the job's training process? Both the `check_*()` functions provide detailed information about the\n",
    "\n",
    "Base model (EleutherAI/pythia-410m-v0):\n",
    "\n",
    "\n",
    "\n",
    "The `check_job_status()` method is a simple wrapper around the `check_job_status` function. It returns a `JobStatus` object that can be used to monitor the status of a job.\n",
    "\n",
    "The `check_job` method is a simple wrapper around the check_job_status method. It returns a `Job` object that can be used to check the status of a job."
   ]
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    "大家也可以看一下原版本EleutherAI/pythia-410m的效果  我这边也存了\n",
    "app.lamini.ai/share?model_id=c8ff4b19807dd10007a7f3b51ccc09dd8237ef3d47410dae13394fc072a12978\n",
    "\n",
    "\n",
    "1. Does Lamini have the ability to understand and generate code for audio processing tasks?\n",
    "\n",
    "This model:\n",
    "\n",
    "Yes, Lamini has the ability to understand and generate code.\n",
    "\n",
    "Base model (EleutherAI/pythia-410m):\n",
    "\n",
    "\n",
    "\n",
    "A:\n",
    "\n",
    "Lamini is a very good language for audio processing.\n",
    "\n",
    "A:\n",
    "\n",
    "I think you are looking for a language that can be used to write audio code.\n",
    "\n",
    "A:\n",
    "\n",
    "Languages like C, C++, Java, Python, C#, C++, C++ and others are good for audio coding.\n",
    "\n",
    "A:\n",
    "\n",
    "You can use a language like C, C++, Java, C#, C++, C++ or C++ for audio coding.\n",
    "\n",
    "A language that can be used to write code for audio coding is C.\n",
    "\n",
    "A:\n",
    "\n",
    "C is a good language for audio coding.\n",
    "\n",
    "A good language for audio coding is C.\n",
    "C++ is a good language for audio coding, but it is not a good language for audio coding.\n",
    "\n",
    "\n",
    "2. Is it possible to control the level of detail in the generated output?\n",
    "\n",
    "This model:\n",
    "\n",
    "Yes, it is possible to control the level of detail provided in the generated output. To do so, you can use the \"level\" parameter in the \"generate_output\" method. This parameter controls the level of detail in the generated text. The default value is \"none\".\n",
    "\n",
    "Base model (EleutherAI/pythia-410m):\n",
    "\n",
    "\n",
    "\n",
    "A:\n",
    "\n",
    "You can use the following code to control the level of detail in your output:\n",
    "#include <iostream>\n",
    "#include <string>\n",
    "#include <vector>\n",
    "\n",
    "using namespace std;\n",
    "\n",
    "int main()\n",
    "{\n",
    "string s;\n",
    "s = \"Hello World\";\n",
    "cout << s << endl;\n",
    "return 0;\n",
    "}\n",
    "\n",
    "Output:\n",
    "Hello World\n",
    "\n",
    "A:\n",
    "\n",
    "You could use the following code to control the output level:\n",
    "#include <iostream.h>\n",
    "#include <string>\n",
    "\n",
    "using namespace std;\n",
    "\n",
    "int main()\n",
    "{\n",
    "string s;\n",
    "s = \"Hello World\";\n",
    "cout << s << endl;\n",
    "return 0;\n",
    "}\n",
    "\n",
    "Output:\n",
    "Hello world\n",
    "\n",
    "A:\n",
    "\n",
    "You should use the following code to control the number of characters in the output:\n",
    "#include <iomanip>\n",
    "#include <string>\n",
    "\n",
    "using namespace std;\n",
    "\n",
    "void main()\n",
    "{\n",
    "string str;\n",
    "str = \"Hello World\";\n",
    "cin >> str;\n",
    "cout << str << endl;\n",
    "}\n",
    "\n",
    "Output:\n",
    "\n",
    "\n",
    "3. Are there any guidelines on using Lamini for generating content in storytelling applications?\n",
    "\n",
    "This model:\n",
    "\n",
    "Yes, there are guidelines on using Lamini for generation of content in storytelling applications. These guidelines include using natural language processing techniques, using clear and concise language, and using clear and concise descriptions.\n",
    "\n",
    "Base model (EleutherAI/pythia-410m):\n",
    "\n",
    "\n",
    "\n",
    "A:\n",
    "\n",
    "I would recommend using Lamini for generating content for your stories.\n",
    "\n",
    "Lamini is a great tool for generating content for your stories. It is a powerful tool for generating content for your stories, but it is not a requirement.\n",
    "\n",
    "Lamini is not a requirement for generating content for your stories. You can use any tool that you want to generate content for your stories.\n",
    "\n",
    "A:\n",
    "\n",
    "I use Lamini for generating content for my stories.\n",
    "\n",
    "Lamini can generate content for your stories. It is not a requirement.\n",
    "\n",
    "A:\n",
    "\n",
    "Lamini is a powerful tool for generating content. It is not a requirement.\n"
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